(1) Field of the Invention
The present invention relates generally to the field of radar and sonar systems. In particular, the invention employs an algorithm in a process for enhanced target detection and tracking.
(2) Description of the Prior Art
State of the art combat systems rely heavily on target motion analysis (TMA) subcomponents. A target motion analysis subcomponent estimates the current position and velocity components of a contact. Estimates from target motion analysis are important to combat personnel because the estimate allows the personnel to predict the location of the hostile contact some time in the future. Precise determination of the future position of the contact is required for accurate targeting of weapons systems as well as for defensive maneuvering and evasion of the contact by friendly units.
In both radar and sonar detection systems, an antenna array receives a reflected signal. Preliminary processing then occurs and the locations of contacts are generated. An example of this type of processing is disclosed in Chang et al., Active Sonar Range-Beam Partitioner, U.S. Pat. No. 5,737,249 (Filed 7 Apr. 1998).
The next stage in processing is to determine range and bearing estimates for each target. Prior attempts have led to two distinct approaches for these determinations. The first approach, (sequential algorithms) uses an averaged measurement to reflect historic information and combines this average in a weighted manner with the most current measurement. This approach yields minimal computational needs due to the small size of the input dataset. Sequential algorithms also can respond quickly to targets that have rapidly varying direction of movement. However, the condensation of all historic measurements into a single set of input numbers results in a great loss in the granularity of the data. Sequential algorithms have not been able to utilize the complete historic dataset to dynamically recompute the output range and bearing as a cache set of input values is received.
Batch processing algorithms have developed to meet this precise need. However, batch processing algorithms have also been plagued with a plethora of problems. First, computational requirements have consistently been exceptionally high. As a result, algorithm designers have been limited in the amount of processing steps which could be performed while still providing real time output. In some circumstances, computational needs have been so high as to require limiting the number of individual historic input measurements which are processed. As such, all viable prior attempts have used a single stage algorithm for processing.
The first type of algorithm often used is grid searching. The grid search technique divides the target space into a number of cells. Contact range and bearing are computed by detecting movement between cells. In order for this technique to be successful, the resolution of the target grid must be very fine. This fine resolution has resulted in extreme computational power requirements.
The second type of algorithm is a stand-alone endpoint coordinate maximum likelihood estimation (MLE). In maximum likelihood estimation, an iterative least-squares technique is used to determine contact range and bearing. However, this approach has been subject to over-sensitivity, especially in cases where iterations on the quadratic solution lead to a divergence rather than a convergence.